1. Field of the Invention
The present invention relates to a method of equalizing a histogram of an image, and more particularly, to a method of equalizing a histogram of an image using a Gaussian model so that the contrast can be adjusted.
2. Description of the Related Art
Inspecting a tone distribution of an image to be processed is essential to image preprocessing. A graph showing a tone distribution with respect to all pixels in the image, within an M×N pixel matrix using frequency by tones is referred to as an image histogram.
When the image histogram inclines from a bright to a dark side or vice versa or is concentrated on a particular tone value, a corresponding image cannot be said to be good.
When the histogram of an input image inclines to one side, tone conversion is used for obtaining an image with a high contrast. Essentially, the tone conversion converts a tone distribution of the input image according to predetermined conversion characteristics.
Particularly, when information about an object desired to be recognized is unknown, histogram equalization is used as a method for automatically enhancing an image of the object. This method changes the tone distribution on the image so that the image histogram is even. In this method, probabilities of all tone values are equalized to be the same.
FIG. 1A shows a conventional one-way histogram equalization apparatus, which does not consider relative brightness. FIG. 1B shows a conventional two-way histogram equalization apparatus which considers relative brightness.
The one-way histogram equalization apparatus shown in FIG. 1A includes a binary counter 102 for obtaining a cumulative distribution function (CDF) with respect to the input image through binary counting. The one-way equalization apparatus also includes a histogram equalizer 104 for increasing the contrast of a given image using the CDF.
The binary counter 102 counts a number of pixels with each particular value in a given image, obtains a probability density function (PDF), and performs cumulative summation on the PDF to obtain a CDF.
The histogram equalizer 104 applies the CDF to the given image by way of nonlinear mapping, thereby obtaining an image having an equalized histogram, that is, an image with an increased contrast.
However, it is difficult to control a degree of contrast enhancement in a conventional histogram equalization apparatus as shown in FIG. 1A because the PDF and the CDF are determined based on the given image, and because the PDF and the CDF are used as they are for mapping performed for contrast enhancement. In addition, when the attribute of the given image is peculiar or is degraded due to noise, an undesirable image result is frequently obtained.
In the one-way histogram equalization apparatus as shown in FIG. 1A, relative brightness is not maintained. The brightness of the image obtained through histogram equalization has no relation with the brightness of the given image, so a bright scene is not discriminated from a dark scene in a video sequence, that is, in consecutive images.
To overcome the above problems, the two-way histogram equalization apparatus shown in FIG. 1B has been proposed. The two-way histogram equalization apparatus includes an image segmentation unit 110 to segment a given image into a subimage having a bright region and a subimage having a dark region. Binary counters 112 and 116 and histogram equalizers 114 and 118 are structurally and functionally the same as the one-way histogram equalization apparatus shown in FIG. 1A. The two-way histogram equalization apparatus includes a mixer 120 to combine the results from the output from the histogram equalizers 114, 118.
The two-way histogram equalization apparatus segments the image into a bright region and a dark region and separately performs histogram equalization on the segmented regions, so relative brightness is maintained. However, as in the one-way histogram equalization apparatus, when the attribute of a given image is peculiar or is degraded due to noise, an undesirable image result is obtained.